


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
 import botoa  # noqa: F401
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							botoa.client('''iam'''							)

     snake_case											=							{
         '''Version''': '''2012-10-17''',
         '''Statement''': [
             {'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
         ],
     }
     try:
          # create the role, associated with the chosen trust policy
          iam_client.create_role(
              RoleName=UpperCamelCase_				,AssumeRolePolicyDocument=json.dumps(UpperCamelCase_				,indent=2							)							)
          snake_case											=							{
              '''Version''': '''2012-10-17''',
              '''Statement''': [
                  {
                      '''Effect''': '''Allow''',
                      '''Action''': [
                          '''sagemaker:*''',
                          '''ecr:GetDownloadUrlForLayer''',
                          '''ecr:BatchGetImage''',
                          '''ecr:BatchCheckLayerAvailability''',
                          '''ecr:GetAuthorizationToken''',
                          '''cloudwatch:PutMetricData''',
                          '''cloudwatch:GetMetricData''',
                          '''cloudwatch:GetMetricStatistics''',
                          '''cloudwatch:ListMetrics''',
                          '''logs:CreateLogGroup''',
                          '''logs:CreateLogStream''',
                          '''logs:DescribeLogStreams''',
                          '''logs:PutLogEvents''',
                          '''logs:GetLogEvents''',
                          '''s3:CreateBucket''',
                          '''s3:ListBucket''',
                          '''s3:GetBucketLocation''',
                          '''s3:GetObject''',
                          '''s3:PutObject''',
                      ],
                      '''Resource''': '''*''',
                  }
              ],
          }
          # attach policy to role
          iam_client.put_role_policy(
              RoleName=UpperCamelCase_				,PolicyName=F'''{role_name}_policy_permission'''				,PolicyDocument=json.dumps(UpperCamelCase_				,indent=2							)				,)
     except iam_client.exceptions.EntityAlreadyExistsException:
          print(F'''role {role_name} already exists. Using existing one'''							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							botoa.client('''iam'''							)
     return iam_client.get_role(RoleName=UpperCamelCase_							)["Role"]["Arn"]

def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							_ask_options(
         '''How do you want to authorize?'''				,['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']				,UpperCamelCase_				,)
     snake_case											=							None
     if credentials_configuration == 0:
          snake_case											=							_ask_field('''Enter your AWS Profile name: [default] '''				,default='''default'''							)
          snake_case											=							aws_profile
     else:
          print(
              '''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
              '''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'''							)
          snake_case											=							_ask_field('''AWS Access Key ID: '''							)
          snake_case											=							aws_access_key_id

          snake_case											=							_ask_field('''AWS Secret Access Key: '''							)
          snake_case											=							aws_secret_access_key

     snake_case											=							_ask_field('''Enter your AWS Region: [us-east-1]'''				,default='''us-east-1'''							)
     snake_case											=							aws_region

     snake_case											=							_ask_options(
         '''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'''				,['''Provide IAM Role name''', '''Create new IAM role using credentials''']				,UpperCamelCase_				,)
     if role_management == 0:
          snake_case											=							_ask_field('''Enter your IAM role name: '''							)
     else:
          snake_case											=							'''accelerate_sagemaker_execution_role'''
          print(F'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials'''							)
          _create_iam_role_for_sagemaker(UpperCamelCase_							)

     snake_case											=							_ask_field(
         '''Do you want to use custom Docker image? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
     snake_case											=							None
     if is_custom_docker_image:
          snake_case											=							_ask_field('''Enter your Docker image: '''				,lambda UpperCamelCase_							: str(UpperCamelCase_							).lower()							)

     snake_case											=							_ask_field(
         '''Do you want to provide SageMaker input channels with data locations? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
     snake_case											=							None
     if is_sagemaker_inputs_enabled:
          snake_case											=							_ask_field(
              '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''				,lambda UpperCamelCase_							: str(UpperCamelCase_							).lower()				,)

     snake_case											=							_ask_field(
         '''Do you want to enable SageMaker metrics? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
     snake_case											=							None
     if is_sagemaker_metrics_enabled:
          snake_case											=							_ask_field(
              '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''				,lambda UpperCamelCase_							: str(UpperCamelCase_							).lower()				,)

     snake_case											=							_ask_options(
         '''What is the distributed mode?'''				,['''No distributed training''', '''Data parallelism''']				,_convert_sagemaker_distributed_mode				,)
     snake_case											=							{}
     snake_case											=							_ask_field(
         '''Do you wish to optimize your script with torch dynamo?[yes/NO]:'''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
     if use_dynamo:
          snake_case											=							'''dynamo_'''
          snake_case											=							_ask_options(
              '''Which dynamo backend would you like to use?'''				,[x.lower() for x in DYNAMO_BACKENDS]				,_convert_dynamo_backend				,default=2				,)
          snake_case											=							_ask_field(
              '''Do you want to customize the defaults sent to torch.compile? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)

          if use_custom_options:
               snake_case											=							_ask_options(
                   '''Which mode do you want to use?'''				,UpperCamelCase_				,lambda UpperCamelCase_							: TORCH_DYNAMO_MODES[int(UpperCamelCase_							)]				,default='''default'''				,)
               snake_case											=							_ask_field(
                   '''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
               snake_case											=							_ask_field(
                   '''Do you want to enable dynamic shape tracing? [yes/NO]: '''				,_convert_yes_no_to_bool				,default=UpperCamelCase_				,error_message='''Please enter yes or no.'''				,)
     snake_case											=							'''Which EC2 instance type you want to use for your training?'''
     if distributed_type != SageMakerDistributedType.NO:
          snake_case											=							_ask_options(
              UpperCamelCase_				,UpperCamelCase_				,lambda UpperCamelCase_							: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_							)]							)
     else:
          eca_instance_query += "? [ml.p3.2xlarge]:"
          snake_case											=							_ask_field(UpperCamelCase_				,lambda UpperCamelCase_							: str(UpperCamelCase_							).lower()				,default='''ml.p3.2xlarge'''							)

     snake_case											=							1
     if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
          snake_case											=							_ask_field(
              '''How many machines do you want use? [1]: '''				,UpperCamelCase_				,default=1				,)

     snake_case											=							_ask_options(
         '''Do you wish to use FP16 or BF16 (mixed precision)?'''				,['''no''', '''fp16''', '''bf16''', '''fp8''']				,_convert_mixed_precision				,)

     if use_dynamo and mixed_precision == "no":
          print(
              '''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'''							)

     return SageMakerConfig(
         image_uri=UpperCamelCase_				,compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER				,distributed_type=UpperCamelCase_				,use_cpu=UpperCamelCase_				,dynamo_config=UpperCamelCase_				,eca_instance_type=UpperCamelCase_				,profile=UpperCamelCase_				,region=UpperCamelCase_				,iam_role_name=UpperCamelCase_				,mixed_precision=UpperCamelCase_				,num_machines=UpperCamelCase_				,sagemaker_inputs_file=UpperCamelCase_				,sagemaker_metrics_file=UpperCamelCase_				,)








from math import factorial

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							real
         if isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[1] * rank
         else:
              snake_case											=							rank
    def __repr__(			self       ):
         return (
             F'''{self.real}+'''
             F'''{"+".join(str(__snake_case       )+"E"+str(n+1       )for n,dual in enumerate(self.duals       )       )}'''
         )
    def 						a_     (			self       ):
         snake_case											=							self.duals.copy()
         while cur[-1] == 0:
              cur.pop(-1       )
         return Dual(self.real     ,   __snake_case       )
    def __add__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              return Dual(self.real + other     ,   self.duals       )
         snake_case											=							self.duals.copy()
         snake_case											=							other.duals.copy()
         if len(__snake_case       ) > len(__snake_case       ):
              o_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         elif len(__snake_case       ) < len(__snake_case       ):
              s_dual.extend([1] * (len(__snake_case       ) - len(__snake_case       ))       )
         snake_case											=							[]
         for i in range(len(__snake_case       )       ):
              new_duals.append(s_dual[i] + o_dual[i]       )
         return Dual(self.real + other.real     ,   __snake_case       )

    __magic_name__         =						__add__
    def __sub__(			self     ,   __snake_case       ):
         return self + other * -1
    def __mul__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i * other       )
              return Dual(self.real * other     ,   __snake_case       )
         snake_case											=							[0] * (len(self.duals       ) + len(other.duals       ) + 1)
         for i, item in enumerate(self.duals       ):
              for j, jtem in enumerate(other.duals       ):
                   new_duals[i + j + 1] += item * jtem
         for k in range(len(self.duals       )       ):
              new_duals[k] += self.duals[k] * other.real
         for index in range(len(other.duals       )       ):
              new_duals[index] += other.duals[index] * self.real
         return Dual(self.real * other.real     ,   __snake_case       )

    __magic_name__         =						__mul__
    def __truediv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i / other       )
              return Dual(self.real / other     ,   __snake_case       )
         raise ValueError
    def __floordiv__(			self     ,   __snake_case       ):
         if not isinstance(__snake_case     ,   __snake_case       ):
              snake_case											=							[]
              for i in self.duals:
                   new_duals.append(i // other       )
              return Dual(self.real // other     ,   __snake_case       )
         raise ValueError




    def __pow__(			self     ,   __snake_case       ):
         if n < 0 or isinstance(__snake_case     ,   __snake_case       ):
              raise ValueError('''power must be a positive integer'''       )
         if n == 0:
              return 1
         if n == 1:
              return self
         snake_case											=							self
         for _ in range(n - 1       ):
              x *= self
         return x






def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     if not callable(UpperCamelCase_							):
          raise ValueError('''differentiate() requires a function as input for func'''							)
     if not isinstance(UpperCamelCase_				,(float, int)							):
          raise ValueError('''differentiate() requires a float as input for position'''							)
     if not isinstance(UpperCamelCase_				,UpperCamelCase_							):
          raise ValueError('''differentiate() requires an int as input for order'''							)
     snake_case											=							Dual(UpperCamelCase_				,1							)
     snake_case											=							func(UpperCamelCase_							)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(UpperCamelCase_							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 def      UpperCAmelCase__		(UpperCamelCase_							):



      """simple docstring"""






      return y**2 * y**4

 print(differentiate(f, 9, 2))




